The invention relates to the management of large stationary batteries, and incorporates a method of predictive battery failure analysis to enable users to more accurately estimate the useful life of a battery or series of batteries. The invention is preferably employed within a comprehensive system and apparatus for the management of stationary batteries that are used for backup power and are deployed in widely dispersed locations. The apparatus and system preferably is comprised of battery tags, sometimes referred to herein as “Mega-Tags,” which are preferably serialized bar-coded identification labels or radio frequency identification (RFID) tags, a battery testing and data acquisition device compatible with the type or types of Mega Tags employed, and web-based software (a part of the OMS® battery management system) to perform the method and yield the useful result back to the user, to a field technician or to any other designated recipients. These components work together to provide a platform for managing a large number of perishable, expensive, and geographically dispersed assets and plan for the anticipated failure of the units.
Thus the invention solves one of the most important problems associated with batteries. Batteries are perishable, and predicting their useful lifespan to help manage servicing schedules, replacement cost and budgeting for uninterrupted power service. Stationary industrial batteries of the type that benefit from the invention are typically sealed lead-acid batteries. These electromechanical devices typically must be installed within 6-10 months from date of manufacture or else they need to be recharged. In addition, most of these batteries are designed for a 10 year useful life, but in the field generally last only from 2-6 years. The discrepancy between design life and actual life is a major problem for users of these batteries.
Batteries are generally deployed in strings or units of two or four 12-volt jars, in strings of three 12-volt jars, or in strings of six or twelve 2-volt jars, in order to power 24 volt, 36 volt or 48 volt equipment. Other string configurations are also possible. This electrical combination of batteries compounds the difficulty of managing these storage devices. In sum, managing stationary batteries is difficult, and is generally not a core competency of most businesses that use these batteries.
In order to have visibility into the state of health of a stationary battery plant, it is necessary to periodically test the batteries. Upon testing, gross failure of the batteries is apparent, as would be an impending failure within a short time horizon, such as a week or two. These short-term indicia of impending battery failure do not allow planning and budgeting to replace critical power assets.
In many cases, even where the batteries are in good condition there may be a deficiency of backup power due to changes in load requirements. This situation occurs frequently in many industries. For example, in a wireless telecommunications application, a battery backup plant might initially provide four hours of runtime; yet over time as additional communications gear is added to the transmission location, the batteries have to power a greater load and therefore provide less runtime. (Note that “runtime” refers to the length of time that the backup system can provide adequate power to keep the primary equipment operational.)
Prior methods of predicting battery failure are limited to predicting an imminent failure by monitoring a battery jar. For example, U.S. Pat. No. 5,250,904 to Salander et al. provides a prediction of imminent battery failure by means of monitoring a slight change in voltage across the cell or battery terminals, while it is in “float” service, i.e., neither charging or discharging under use. Again, such a short horizon of impending battery failure does not allow planning and budgeting to replace critical power assets.